Feature Extraction and Machine Learning for the Classification of Brazilian Savannah Pollen Grains

The classification of pollen species and types is an important task in many areas like forensic palynology, archaeological palynology and melissopalynology. This paper presents the first annotated image dataset for the Brazilian Savannah pollen types that can be used to train and test computer visio...

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Vydáno v:	PloS one Ročník 11; číslo 6; s. e0157044
Hlavní autoři:	Gonçalves, Ariadne Barbosa, Souza, Junior Silva, Silva, Gercina Gonçalves da, Cereda, Marney Pascoli, Pott, Arnildo, Naka, Marco Hiroshi, Pistori, Hemerson
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Public Library of Science 08.06.2016 Public Library of Science (PLoS)
Témata:	Accuracy Archaeology Artificial intelligence Automation Biology and Life Sciences Biotechnology Brazil Classification Computer and Information Sciences Computer science Computer vision Criminal investigations Data mining Environmental science Feature extraction Forensic engineering Forensic science Grassland Honey Human performance Identification Identification and classification Information retrieval Learning algorithms Machine Learning Medicine and Health Sciences Palynology Physiological aspects Pollen Pollen - anatomy & histology Pollen - classification Research and Analysis Methods Researchers Savannahs Species classification Sustainability Wavelet transforms Brazil
ISSN:	1932-6203, 1932-6203
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Abstract	The classification of pollen species and types is an important task in many areas like forensic palynology, archaeological palynology and melissopalynology. This paper presents the first annotated image dataset for the Brazilian Savannah pollen types that can be used to train and test computer vision based automatic pollen classifiers. A first baseline human and computer performance for this dataset has been established using 805 pollen images of 23 pollen types. In order to access the computer performance, a combination of three feature extractors and four machine learning techniques has been implemented, fine tuned and tested. The results of these tests are also presented in this paper.
AbstractList	The classification of pollen species and types is an important task in many areas like forensic palynology, archaeological palynology and melissopalynology. This paper presents the first annotated image dataset for the Brazilian Savannah pollen types that can be used to train and test computer vision based automatic pollen classifiers. A first baseline human and computer performance for this dataset has been established using 805 pollen images of 23 pollen types. In order to access the computer performance, a combination of three feature extractors and four machine learning techniques has been implemented, fine tuned and tested. The results of these tests are also presented in this paper. The classification of pollen species and types is an important task in many areas like forensic palynology, archaeological palynology and melissopalynology. This paper presents the first annotated image dataset for the Brazilian Savannah pollen types that can be used to train and test computer vision based automatic pollen classifiers. A first baseline human and computer performance for this dataset has been established using 805 pollen images of 23 pollen types. In order to access the computer performance, a combination of three feature extractors and four machine learning techniques has been implemented, fine tuned and tested. The results of these tests are also presented in this paper.The classification of pollen species and types is an important task in many areas like forensic palynology, archaeological palynology and melissopalynology. This paper presents the first annotated image dataset for the Brazilian Savannah pollen types that can be used to train and test computer vision based automatic pollen classifiers. A first baseline human and computer performance for this dataset has been established using 805 pollen images of 23 pollen types. In order to access the computer performance, a combination of three feature extractors and four machine learning techniques has been implemented, fine tuned and tested. The results of these tests are also presented in this paper.
Audience	Academic
Author	Souza, Junior Silva Gonçalves, Ariadne Barbosa Naka, Marco Hiroshi Pistori, Hemerson Pott, Arnildo Silva, Gercina Gonçalves da Cereda, Marney Pascoli
AuthorAffiliation	2 Department of Computing Science, Universidade Federal de Mato Grosso do Sul, Campo Grande, Mato Grosso do Sul, Brazil 3 Department of Environmental Science and Agricultural Sustainability, Dom Bosco Catholic University, Campo Grande, Mato Grosso do Sul, Brazil 4 Laboratory of Botany, Universidade Federal de Mato Grosso do Sul, Campo Grande, Mato Grosso do Sul, Brazil 1 Department of Biotechnology, INOVISAO, Dom Bosco Catholic University, Campo Grande, Mato Grosso do Sul, Brazil 5 Direction of Research, Extension and Institutional, Federal Institute of Mato Grosso do Sul, Science and Technology. Campo Grande, Mato Grosso do Sul, Brazil University of Ulm, GERMANY
AuthorAffiliation_xml	– name: 3 Department of Environmental Science and Agricultural Sustainability, Dom Bosco Catholic University, Campo Grande, Mato Grosso do Sul, Brazil – name: 4 Laboratory of Botany, Universidade Federal de Mato Grosso do Sul, Campo Grande, Mato Grosso do Sul, Brazil – name: 1 Department of Biotechnology, INOVISAO, Dom Bosco Catholic University, Campo Grande, Mato Grosso do Sul, Brazil – name: 2 Department of Computing Science, Universidade Federal de Mato Grosso do Sul, Campo Grande, Mato Grosso do Sul, Brazil – name: 5 Direction of Research, Extension and Institutional, Federal Institute of Mato Grosso do Sul, Science and Technology. Campo Grande, Mato Grosso do Sul, Brazil – name: University of Ulm, GERMANY
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Cites_doi	10.1145/1656274.1656278 10.1016/j.jfoodeng.2012.03.028 10.1023/A:1021322813565 10.1016/j.patrec.2005.10.010 10.1016/S0277-3791(99)00021-9 10.1016/j.procs.2013.05.137 10.1007/978-3-642-41181-6_72 10.1016/0034-6667(90)90133-4 10.1109/TSMC.1973.4309314 10.1145/1290082.1290111 10.1109/TSMCC.2005.855426
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Copyright	COPYRIGHT 2016 Public Library of Science 2016 Gonçalves et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2016 Gonçalves et al 2016 Gonçalves et al
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Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Conceived and designed the experiments: JSS ABG HP. Performed the experiments: GGS ABG AP. Analyzed the data: MPC ABG HP. Contributed reagents/materials/analysis tools: AP MPC MHN. Wrote the paper: ABG JSS GGS MPC AP MHN HP. Competing Interests: The authors have declared that no competing interests exist. These authors also contributed equally to this work.
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SubjectTerms	Accuracy Archaeology Artificial intelligence Automation Biology and Life Sciences Biotechnology Brazil Classification Computer and Information Sciences Computer science Computer vision Criminal investigations Data mining Environmental science Feature extraction Forensic engineering Forensic science Grassland Honey Human performance Identification Identification and classification Information retrieval Learning algorithms Machine Learning Medicine and Health Sciences Palynology Physiological aspects Pollen Pollen - anatomy & histology Pollen - classification Research and Analysis Methods Researchers Savannahs Species classification Sustainability Wavelet transforms
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Title	Feature Extraction and Machine Learning for the Classification of Brazilian Savannah Pollen Grains
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Volume	11
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